{"meta":{"query_hash":"82469fcbe380","filters":{"venue":"International Journal of Computational Bioscience"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/82469fcbe380","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Computational+Bioscience"},"results":[{"id":"W2095063295","doi":"10.2316/journal.210.2010.1.210-1010","title":"COMPARISON OF CHEMICAL DESCRIPTORS FOR PROTEIN–CHEMICAL INTERACTION PREDICTION","year":2010,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; National Science Foundation","keywords":"Computer science","score_opus":0.03603925648432237,"score_gpt":0.38140282040214496,"score_spread":0.3453635639178226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095063295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46486965,0.00000979113,0.5316569,0.0008502809,0.0024005887,0.000118173084,0.00001798445,0.000017138167,0.000059452246],"genre_scores_gemma":[0.65949935,3.728657e-7,0.3401494,0.00006494909,0.00026027247,0.0000065390573,0.000009569064,0.0000054336692,0.000004091632],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970413,0.00006711148,0.0010239528,0.00029557504,0.0013946184,0.00017743526],"domain_scores_gemma":[0.9956925,0.00066637975,0.0009865351,0.0001553492,0.00237077,0.000128494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000941382,0.00015359452,0.0002703587,0.00043334818,0.000052556188,0.00017002271,0.0017415949,0.00008471194,0.0000132373225],"category_scores_gemma":[0.0011330999,0.00014452296,0.00022214963,0.00037582387,0.00022036275,0.0013689044,0.00020060233,0.00035231412,0.0000031162042],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016581737,0.0005126497,0.0014527619,0.000015367898,0.00006982619,0.0000035840826,0.0004310355,0.04268848,0.85323864,0.074811615,0.0004415129,0.026168704],"study_design_scores_gemma":[0.0007404388,0.00018445218,0.0019115654,0.000071275594,0.000010899465,0.00019542339,0.000038556613,0.49344146,0.44232786,0.060018256,0.00090780726,0.00015199742],"about_ca_topic_score_codex":0.0000032643813,"about_ca_topic_score_gemma":4.4483437e-7,"teacher_disagreement_score":0.45075297,"about_ca_system_score_codex":0.000118010605,"about_ca_system_score_gemma":0.0004623894,"threshold_uncertainty_score":0.58934754},"labels":[],"label_agreement":null},{"id":"W2112611875","doi":"10.2316/j.2010.210-1023","title":"ALIGNMENT-BASED EXTENSION TO DDPIN FEATURE EXTRACTION","year":2010,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Grantová Agentura České Republiky","keywords":"Extension (predicate logic); Computer science; Feature extraction; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Programming language; Linguistics","score_opus":0.005835325165920761,"score_gpt":0.3079265576214354,"score_spread":0.3020912324555146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112611875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7512014,0.000013601031,0.24036163,0.0057568424,0.0022036901,0.000065516426,0.000011288818,0.000006224495,0.00037979995],"genre_scores_gemma":[0.8658679,0.0000023352268,0.13176231,0.0018686705,0.00036701115,9.3677806e-7,0.000024954898,0.0000054155234,0.000100452926],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998826,0.000023260993,0.0002592917,0.00012705724,0.0006632084,0.00010114546],"domain_scores_gemma":[0.99870926,0.000041486288,0.00027817054,0.00009670395,0.00077133725,0.000103048646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039389494,0.000085969274,0.00007096101,0.00016833397,0.00005739973,0.00007517092,0.0004585225,0.00006466075,0.000031561423],"category_scores_gemma":[0.00049536995,0.000074867385,0.00007205447,0.000103372986,0.000062352396,0.000021558257,0.00006220764,0.00019428258,0.000016682628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012714448,0.000082596845,0.002120902,0.000002636233,0.00002025855,0.000009687291,0.000039193976,0.09870267,0.88556784,0.00046662762,0.0056195143,0.007240941],"study_design_scores_gemma":[0.0024236294,0.0014203166,0.16994648,0.00013056521,0.000027288499,0.001938225,0.00010622708,0.08856227,0.4091561,0.0021344598,0.32347468,0.0006797543],"about_ca_topic_score_codex":0.0000019783424,"about_ca_topic_score_gemma":0.0000036014205,"teacher_disagreement_score":0.47641173,"about_ca_system_score_codex":0.000021698235,"about_ca_system_score_gemma":0.00018013378,"threshold_uncertainty_score":0.30530035},"labels":[],"label_agreement":null},{"id":"W2316739831","doi":"10.2316/j.2013.210-1055","title":"SIGNAL ANALYSIS OF MULTI-PARAMETRIC MR IMAGES IN HIGHER ORDER FOURIER SPACES","year":2013,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fourier analysis; Fourier transform; SIGNAL (programming language); Computer science; Parametric statistics; Order (exchange); Computer vision; Artificial intelligence; Mathematics; Mathematical analysis; Statistics","score_opus":0.027994942117398086,"score_gpt":0.32813263214338995,"score_spread":0.3001376900259919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316739831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05369026,0.000086158616,0.94255304,0.0029430087,0.0004621926,0.00009862155,0.0000070838582,0.000019444458,0.00014020535],"genre_scores_gemma":[0.56684613,0.000011908672,0.4326051,0.00043037772,0.000029013485,0.0000032690164,0.0000025833906,0.0000027202993,0.00006890528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99683344,0.000113072194,0.0008613233,0.0002473499,0.0017660941,0.00017874356],"domain_scores_gemma":[0.99621063,0.00055722945,0.00077110075,0.00014036785,0.0021928006,0.0001278867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072565227,0.00012832665,0.00029474444,0.002513418,0.000032016982,0.00026392395,0.0017606698,0.000048010083,0.0005318571],"category_scores_gemma":[0.00038349006,0.00010712447,0.00015453306,0.0034419196,0.00026240954,0.0016720458,0.00019335862,0.0001787601,0.00001562085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008817008,0.0032786147,0.11254729,0.000039222556,0.0021142412,0.0003481571,0.0021683264,0.4508433,0.08612455,0.020206414,0.006691017,0.31555068],"study_design_scores_gemma":[0.0011076991,0.00019244579,0.3513757,0.00007822279,0.000061093066,0.000046462686,0.000074258685,0.6131979,0.024342544,0.009115846,0.00012330477,0.0002845221],"about_ca_topic_score_codex":0.00006962874,"about_ca_topic_score_gemma":0.0000027370268,"teacher_disagreement_score":0.5131559,"about_ca_system_score_codex":0.000109570785,"about_ca_system_score_gemma":0.000231512,"threshold_uncertainty_score":0.5823462},"labels":[],"label_agreement":null},{"id":"W2322004849","doi":"10.2316/j.2010.210-1025","title":"DISCRETE APPROACHES FOR SOLVING MOLECULAR DISTANCE GEOMETRY PROBLEMS USING NMR DATA","year":2010,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centre National de la Recherche Scientifique; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Geometry; Computer science; Mathematics","score_opus":0.042759805647581604,"score_gpt":0.369556227532949,"score_spread":0.3267964218853674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322004849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2072429,0.000022391483,0.79104155,0.0008094591,0.00038694445,0.00009396212,0.00019021376,0.0000045537618,0.00020803306],"genre_scores_gemma":[0.8516583,5.464384e-7,0.14779198,0.000049177223,0.00040799897,0.000003886769,0.00006676218,0.000006734281,0.000014604672],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883276,0.000008433091,0.0003404267,0.00020948898,0.00046799058,0.00014087341],"domain_scores_gemma":[0.99886423,0.00012943835,0.00040056347,0.00016283302,0.0003711988,0.00007174518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035285464,0.000093614806,0.000108558095,0.00011658889,0.00013393752,0.00017773977,0.0011662239,0.00001906639,0.000038130394],"category_scores_gemma":[0.000042014857,0.00008343204,0.000076553486,0.00016902546,0.00013948379,0.0005700065,0.00013367026,0.00017709748,0.0000019903184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040157833,0.00033847612,0.011589963,0.000011869654,0.00018495234,0.0000034942,0.0001571871,0.18461776,0.15970929,0.6318016,0.0002802997,0.011264969],"study_design_scores_gemma":[0.0007541495,0.000051376355,0.0031444877,0.00006815993,0.000039950035,0.00005373563,0.00012925031,0.7138577,0.013823852,0.26240924,0.005380861,0.0002872224],"about_ca_topic_score_codex":0.000010752971,"about_ca_topic_score_gemma":0.0000015942034,"teacher_disagreement_score":0.6444154,"about_ca_system_score_codex":0.000022153066,"about_ca_system_score_gemma":0.00019895114,"threshold_uncertainty_score":0.340226},"labels":[],"label_agreement":null},{"id":"W2325128228","doi":"10.2316/j.2012.210-1029","title":"SAMPLE SIZE ESTIMATION FOR CANCER PROGRESSION MODELS","year":2012,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung","keywords":"Estimation; Sample size determination; Sample (material); Cancer; Medicine; Statistics; Oncology; Mathematics; Internal medicine; Engineering; Physics","score_opus":0.5373077102737298,"score_gpt":0.6291711456384945,"score_spread":0.09186343536476471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325128228","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013980856,0.00006418829,0.9806013,0.0019485115,0.0029746525,0.00019600322,0.00013416707,0.000015455546,0.000084837884],"genre_scores_gemma":[0.37366864,0.000009175176,0.62556505,0.00021324672,0.0005134805,0.000011813427,9.464168e-7,0.000006981982,0.000010670934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975857,0.00012872645,0.00083929085,0.00012747335,0.0011191677,0.0001996219],"domain_scores_gemma":[0.95404154,0.043561846,0.0008204035,0.00006880609,0.0013576987,0.00014970523],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0026903138,0.00010605156,0.00023960027,0.00011098631,0.00007436119,0.000067343775,0.00048484092,0.000056674944,0.000102110884],"category_scores_gemma":[0.05951766,0.00008224299,0.00014094343,0.00014064189,0.00013729528,0.00061436626,0.00005985696,0.000118582524,0.0000024097483],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052572927,0.000839379,0.0016890327,0.000053482152,0.00017474286,0.000003438779,0.00049464713,0.0540649,0.0010767928,0.7265732,0.0033361816,0.21116842],"study_design_scores_gemma":[0.0005910018,0.00008411426,0.0011488271,0.00010655913,0.000026275931,0.00002380281,0.000014394198,0.10027237,0.0009871027,0.8963511,0.0003064503,0.00008799275],"about_ca_topic_score_codex":0.0000028879288,"about_ca_topic_score_gemma":2.748054e-7,"teacher_disagreement_score":0.3596878,"about_ca_system_score_codex":0.00012538349,"about_ca_system_score_gemma":0.00018231822,"threshold_uncertainty_score":0.94840443},"labels":[],"label_agreement":null},{"id":"W2335253323","doi":"10.2316/journal.210.2010.1.210-1013","title":"AN INTEGRATED BIOINFORMATICS APPROACH TO THE DISCOVERY OF CIS -REGULATORY ELEMENTS INVOLVED IN PLANT GRAVITROPIC SIGNAL TRANSDUCTION","year":2010,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Signal transduction; SIGNAL (programming language); Computational biology; Biology; Plant growth; Bioinformatics; Cell biology; Computer science; Botany","score_opus":0.010289568888765725,"score_gpt":0.24718500086932227,"score_spread":0.23689543198055654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335253323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.935766,0.000029574634,0.06332465,0.00020590078,0.0004984188,0.00007421897,0.00006883689,5.5434634e-7,0.000031883563],"genre_scores_gemma":[0.9749426,0.000013322525,0.024700949,0.00015900478,0.00013330375,0.0000021860917,0.000039893486,0.0000033942338,0.0000053095087],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989911,0.000028311575,0.0004134503,0.000103333165,0.0003775647,0.00008625864],"domain_scores_gemma":[0.9992752,0.000018547265,0.00022843147,0.00007934453,0.00035873044,0.000039709157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036845947,0.00007691955,0.00009526736,0.00013862339,0.0000354863,0.000040223815,0.00050054636,0.000033584427,0.0000023775947],"category_scores_gemma":[0.00003446774,0.000053952383,0.000053755884,0.000120124154,0.000116118565,0.00001339252,0.000040548406,0.000097767734,4.504285e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015902315,0.00022499281,0.011702279,0.0000044527687,0.00006137219,9.4241585e-7,0.00045028137,0.06921396,0.91341716,0.00082168163,0.00012706718,0.0038167848],"study_design_scores_gemma":[0.0024775784,0.0017422513,0.6320858,0.00008049472,0.000039071092,0.00023714258,0.0020282897,0.0565498,0.2934144,0.00418347,0.006633967,0.00052772736],"about_ca_topic_score_codex":0.0000103391685,"about_ca_topic_score_gemma":0.000038826005,"teacher_disagreement_score":0.6203835,"about_ca_system_score_codex":0.00001548216,"about_ca_system_score_gemma":0.00015980839,"threshold_uncertainty_score":0.22001143},"labels":[],"label_agreement":null},{"id":"W4234820540","doi":"10.2316/j.2012.210-1044","title":"AN AUTOMATED METHOD TO ESTIMATE FEMORAL SHAPE AND MINERAL MASS","year":2012,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mineral; Environmental science; Geology; Computer science; Materials science; Metallurgy","score_opus":0.015225024937395004,"score_gpt":0.37345089800694115,"score_spread":0.3582258730695461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234820540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44833097,0.00006426103,0.54998875,0.00085600495,0.000580019,0.000016393837,0.000006292398,0.0000644129,0.00009288643],"genre_scores_gemma":[0.68819034,0.0000017614003,0.3113659,0.0002523374,0.00017331858,4.288585e-7,0.0000032922246,0.0000044086423,0.000008177629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890524,0.000032508473,0.0002671768,0.00007676528,0.0005697343,0.0001485642],"domain_scores_gemma":[0.9992684,0.00008883097,0.000066594606,0.000036260313,0.00025322917,0.00028668798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005171392,0.00007981596,0.00011659942,0.00024889113,0.0000326846,0.000098016186,0.00028038849,0.000020111933,0.000037121983],"category_scores_gemma":[0.00007780832,0.00006742039,0.000037956608,0.00018399779,0.000044783606,0.0005054879,0.000020199708,0.00008899686,0.0000098505925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008550589,0.0000652324,0.017976835,0.0000070516244,0.000081182254,0.000024420448,0.0003922479,0.87555933,0.062722646,0.0006499961,0.0014488898,0.04106362],"study_design_scores_gemma":[0.00014961291,0.000025027237,0.043537993,0.000024236833,0.000010749947,0.00017082375,0.000021046912,0.95383406,0.0011110102,0.00056915445,0.00046462435,0.00008168814],"about_ca_topic_score_codex":0.000002521071,"about_ca_topic_score_gemma":1.8188874e-7,"teacher_disagreement_score":0.2398594,"about_ca_system_score_codex":0.000042404597,"about_ca_system_score_gemma":0.000027330389,"threshold_uncertainty_score":0.27493238},"labels":[],"label_agreement":null}]}